Antenna Season Report Notebook¶

Josh Dillon, Last Revised January 2022

This notebook examines an individual antenna's performance over a whole season. This notebook parses information from each nightly rtp_summarynotebook (as saved to .csvs) and builds a table describing antenna performance. It also reproduces per-antenna plots from each auto_metrics notebook pertinent to the specific antenna.

In [1]:
import os
from IPython.display import display, HTML
display(HTML("<style>.container { width:100% !important; }</style>"))
In [2]:
# If you want to run this notebook locally, copy the output of the next cell into the next line of this cell.
# antenna = "004"
# csv_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/_rtp_summary_'
# auto_metrics_folder = '/lustre/aoc/projects/hera/H5C/H5C_Notebooks/auto_metrics_inspect'
# os.environ["ANTENNA"] = antenna
# os.environ["CSV_FOLDER"] = csv_folder
# os.environ["AUTO_METRICS_FOLDER"] = auto_metrics_folder
In [3]:
# Use environment variables to figure out path to the csvs and auto_metrics
antenna = str(int(os.environ["ANTENNA"]))
csv_folder = os.environ["CSV_FOLDER"]
auto_metrics_folder = os.environ["AUTO_METRICS_FOLDER"]
print(f'antenna = "{antenna}"')
print(f'csv_folder = "{csv_folder}"')
print(f'auto_metrics_folder = "{auto_metrics_folder}"')
antenna = "59"
csv_folder = "/home/obs/src/H6C_Notebooks/_rtp_summary_"
auto_metrics_folder = "/home/obs/src/H6C_Notebooks/auto_metrics_inspect"
In [4]:
display(HTML(f'<h1 style=font-size:50px><u>Antenna {antenna} Report</u><p></p></h1>'))

Antenna 59 Report

In [5]:
import numpy as np
import pandas as pd
pd.set_option('display.max_rows', 1000)
import glob
import re
from hera_notebook_templates.utils import status_colors, Antenna
In [6]:
# load csvs and auto_metrics htmls in reverse chronological order
csvs = sorted(glob.glob(os.path.join(csv_folder, 'rtp_summary_table*.csv')))[::-1]
print(f'Found {len(csvs)} csvs in {csv_folder}')
auto_metric_htmls = sorted(glob.glob(auto_metrics_folder + '/auto_metrics_inspect_*.html'))[::-1]
print(f'Found {len(auto_metric_htmls)} auto_metrics notebooks in {auto_metrics_folder}')
Found 49 csvs in /home/obs/src/H6C_Notebooks/_rtp_summary_
Found 47 auto_metrics notebooks in /home/obs/src/H6C_Notebooks/auto_metrics_inspect
In [7]:
# Per-season options
mean_round_modz_cut = 4
dead_cut = 0.4
crossed_cut = 0.0

def jd_to_summary_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/_rtp_summary_/rtp_summary_{jd}.html'

def jd_to_auto_metrics_url(jd):
    return f'https://htmlpreview.github.io/?https://github.com/HERA-Team/H6C_Notebooks/blob/main/auto_metrics_inspect/auto_metrics_inspect_{jd}.html'

Load relevant info from summary CSVs¶

In [8]:
this_antenna = None
jds = []

# parse information about antennas and nodes
for csv in csvs:
    df = pd.read_csv(csv)
    for n in range(len(df)):
        # Add this day to the antenna
        row = df.loc[n]
        if isinstance(row['Ant'], str) and '<a href' in row['Ant']:
            antnum = int(row['Ant'].split('</a>')[0].split('>')[-1]) # it's a link, extract antnum
        else:
            antnum = int(row['Ant'])
        if antnum != int(antenna):
            continue
        
        if np.issubdtype(type(row['Node']), np.integer):
            row['Node'] = str(row['Node'])
        if type(row['Node']) == str and row['Node'].isnumeric():
            row['Node'] = 'N' + ('0' if len(row['Node']) == 1 else '') + row['Node']
            
        if this_antenna is None:
            this_antenna = Antenna(row['Ant'], row['Node'])
        jd = [int(s) for s in re.split('_|\.', csv) if s.isdigit()][-1]
        jds.append(jd)
        this_antenna.add_day(jd, row)
        break
In [9]:
# build dataframe
to_show = {'JDs': [f'<a href="{jd_to_summary_url(jd)}" target="_blank">{jd}</a>' for jd in jds]}
to_show['A Priori Status'] = [this_antenna.statuses[jd] for jd in jds]

df = pd.DataFrame(to_show)

# create bar chart columns for flagging percentages:
bar_cols = {}
bar_cols['Auto Metrics Flags'] = [this_antenna.auto_flags[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jee)'] = [this_antenna.dead_flags_Jee[jd] for jd in jds]
bar_cols[f'Dead Fraction in Ant Metrics (Jnn)'] = [this_antenna.dead_flags_Jnn[jd] for jd in jds]
bar_cols['Crossed Fraction in Ant Metrics'] = [this_antenna.crossed_flags[jd] for jd in jds]
bar_cols['Flag Fraction Before Redcal'] = [this_antenna.flags_before_redcal[jd] for jd in jds]
bar_cols['Flagged By Redcal chi^2 Fraction'] = [this_antenna.redcal_flags[jd] for jd in jds]
for col in bar_cols:
    df[col] = bar_cols[col]

z_score_cols = {}
z_score_cols['ee Shape Modified Z-Score'] = [this_antenna.ee_shape_zs[jd] for jd in jds]
z_score_cols['nn Shape Modified Z-Score'] = [this_antenna.nn_shape_zs[jd] for jd in jds]
z_score_cols['ee Power Modified Z-Score'] = [this_antenna.ee_power_zs[jd] for jd in jds]
z_score_cols['nn Power Modified Z-Score'] = [this_antenna.nn_power_zs[jd] for jd in jds]
z_score_cols['ee Temporal Variability Modified Z-Score'] = [this_antenna.ee_temp_var_zs[jd] for jd in jds]
z_score_cols['nn Temporal Variability Modified Z-Score'] = [this_antenna.nn_temp_var_zs[jd] for jd in jds]
z_score_cols['ee Temporal Discontinuties Modified Z-Score'] = [this_antenna.ee_temp_discon_zs[jd] for jd in jds]
z_score_cols['nn Temporal Discontinuties Modified Z-Score'] = [this_antenna.nn_temp_discon_zs[jd] for jd in jds]
for col in z_score_cols:
    df[col] = z_score_cols[col]

ant_metrics_cols = {}
ant_metrics_cols['Average Dead Ant Metric (Jee)'] = [this_antenna.Jee_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Dead Ant Metric (Jnn)'] = [this_antenna.Jnn_dead_metrics[jd] for jd in jds]
ant_metrics_cols['Average Crossed Ant Metric'] = [this_antenna.crossed_metrics[jd] for jd in jds]
for col in ant_metrics_cols:
    df[col] = ant_metrics_cols[col]

redcal_cols = {}
redcal_cols['Median chi^2 Per Antenna (Jee)'] = [this_antenna.Jee_chisqs[jd] for jd in jds]
redcal_cols['Median chi^2 Per Antenna (Jnn)'] = [this_antenna.Jnn_chisqs[jd] for jd in jds]   
for col in redcal_cols:
    df[col] = redcal_cols[col]

# style dataframe
table = df.style.hide_index()\
          .applymap(lambda val: f'background-color: {status_colors[val]}' if val in status_colors else '', subset=['A Priori Status']) \
          .background_gradient(cmap='viridis', vmax=mean_round_modz_cut * 3, vmin=0, axis=None, subset=list(z_score_cols.keys())) \
          .background_gradient(cmap='bwr_r', vmin=dead_cut-.25, vmax=dead_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .background_gradient(cmap='bwr_r', vmin=crossed_cut-.25, vmax=crossed_cut+.25, axis=0, subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .background_gradient(cmap='plasma', vmax=4, vmin=1, axis=None, subset=list(redcal_cols.keys())) \
          .applymap(lambda val: 'font-weight: bold' if val < dead_cut else '', subset=list([col for col in ant_metrics_cols if 'dead' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val < crossed_cut else '', subset=list([col for col in ant_metrics_cols if 'crossed' in col.lower()])) \
          .applymap(lambda val: 'font-weight: bold' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .applymap(lambda val: 'color: red' if val > mean_round_modz_cut else '', subset=list(z_score_cols.keys())) \
          .bar(subset=list(bar_cols.keys()), vmin=0, vmax=1) \
          .format({col: '{:,.4f}'.format for col in z_score_cols}) \
          .format({col: '{:,.4f}'.format for col in ant_metrics_cols}) \
          .format('{:,.2%}', na_rep='-', subset=list(bar_cols.keys())) \
          .set_table_styles([dict(selector="th",props=[('max-width', f'70pt')])]) 

Table 1: Per-Night RTP Summary Info For This Atenna¶

This table reproduces each night's row for this antenna from the RTP Summary notebooks. For more info on the columns, see those notebooks, linked in the JD column.

In [10]:
display(HTML(f'<h2>Antenna {antenna}, Node {this_antenna.node}:</h2>'))
HTML(table.render(render_links=True, escape=False))

Antenna 59, Node N05:

Out[10]:
JDs A Priori Status Auto Metrics Flags Dead Fraction in Ant Metrics (Jee) Dead Fraction in Ant Metrics (Jnn) Crossed Fraction in Ant Metrics Flag Fraction Before Redcal Flagged By Redcal chi^2 Fraction ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score Average Dead Ant Metric (Jee) Average Dead Ant Metric (Jnn) Average Crossed Ant Metric Median chi^2 Per Antenna (Jee) Median chi^2 Per Antenna (Jnn)
2459864 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 50.450052 6.842178 2.077257 -1.089873 3.677281 0.449751 5.471731 12.050349 0.6343 0.6813 0.3855 nan nan
2459863 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 24.406391 5.381905 -0.810319 0.508891 5.804978 3.713988 19.278061 18.066216 0.6490 0.6660 0.3764 nan nan
2459862 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 2.631815 10.328683 0.676779 0.657894 4.714494 3.801800 21.662981 14.692303 0.6836 0.6915 0.3902 nan nan
2459861 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 25.855451 1.532222 -0.720041 0.810004 0.120019 1.529031 2.064155 3.220936 0.6349 0.6900 0.3906 nan nan
2459860 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 21.904884 2.282970 1.054804 0.774635 7.286083 0.971598 3.163818 2.950127 0.6686 0.6839 0.3832 nan nan
2459859 digital_maintenance 100.00% 0.00% 0.00% 0.00% - - 20.057258 1.844305 -0.742431 1.024146 -0.010641 1.563357 5.703602 7.834898 0.6635 0.6850 0.3827 nan nan
2459858 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 27.201683 1.551202 -0.791642 0.865188 0.291481 2.403911 3.708576 5.056602 0.6547 0.6945 0.3951 2.483735 2.420656
2459857 digital_maintenance 0.00% 100.00% 100.00% 0.00% - - 2.431043 -0.630875 -1.325867 -0.642617 1.134683 0.733186 -0.612285 1.017405 0.0288 0.0266 0.0013 nan nan
2459856 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 36.872522 4.123114 1.390223 1.187818 2.715057 0.874903 4.353870 6.058571 0.6456 0.7049 0.3876 2.617801 2.510649
2459855 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 36.769955 5.263141 1.362815 0.624491 1.049044 1.398526 2.532185 3.828587 0.6363 0.7165 0.4213 2.664733 2.585901
2459854 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 43.721341 4.703118 1.820185 0.095387 0.915799 0.913826 1.051646 1.714877 0.6480 0.7450 0.4263 2.443960 2.630338
2459853 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 30.645108 3.140462 2.434389 0.250182 2.991383 0.566680 4.194870 5.984501 0.6741 0.6953 0.3995 2.703891 2.743787
2459852 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 19.368184 3.246582 2.186928 0.127206 5.347286 0.468716 5.050586 0.591528 0.7842 0.8461 0.2432 5.201215 4.633717
2459851 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 21.408526 1.992630 2.651270 0.392886 7.762365 1.898464 5.350345 3.116609 0.7071 0.7542 0.3358 2.799971 2.907872
2459850 digital_maintenance 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 26.038955 3.197912 2.226979 0.241278 3.710470 0.146991 2.885598 2.037742 0.0457 0.0488 0.0014 1.182413 1.178893
2459849 digital_maintenance 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 38.165788 3.916434 5.994384 1.500904 3.409707 1.421007 6.934457 9.013549 0.0469 0.0544 0.0014 1.173547 1.169185
2459848 digital_maintenance 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 24.505006 3.267827 1.917040 2.352809 3.438660 -0.004557 4.790111 4.921548 0.0508 0.0543 0.0018 1.201411 1.201962
2459847 digital_maintenance 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 34.772840 3.457578 2.228304 1.715500 4.486032 1.567689 5.637892 4.807589 0.0408 0.0492 0.0015 1.159278 1.157740
2459846 digital_maintenance 100.00% 100.00% 100.00% 0.00% 100.00% 0.00% 27.210792 3.531416 1.010270 1.399444 4.948944 2.089112 4.175592 3.060530 0.0527 0.0555 0.0026 1.169073 1.178312
2459845 digital_maintenance 100.00% 0.00% 0.00% 0.00% 100.00% 0.00% 23.526629 5.538594 2.335410 2.737583 3.733709 1.254090 22.658554 10.241235 0.7171 0.7530 0.3493 4.915205 5.549201
2459844 digital_maintenance 100.00% 100.00% 100.00% 0.00% - - 4.262356 1.219994 6.765837 1.487968 1.107066 1.292099 -0.687745 0.549580 0.0288 0.0259 0.0021 nan nan
2459843 digital_maintenance 100.00% 0.66% 0.66% 0.00% 100.00% 0.00% 1.270081 19.158184 10.781159 -0.029407 3.770363 1.333786 4.581501 24.254117 0.7635 0.7336 0.3715 4.834336 3.320700

Load antenna metric spectra and waterfalls from auto_metrics notebooks.¶

In [11]:
htmls_to_display = []
for am_html in auto_metric_htmls:
    html_to_display = ''
    # read html into a list of lines
    with open(am_html) as f:
        lines = f.readlines()
    
    # find section with this antenna's metric plots and add to html_to_display
    jd = [int(s) for s in re.split('_|\.', am_html) if s.isdigit()][-1]
    try:
        section_start_line = lines.index(f'<h2>Antenna {antenna}: {jd}</h2>\n')
    except ValueError:
        continue
    html_to_display += lines[section_start_line].replace(str(jd), f'<a href="{jd_to_auto_metrics_url(jd)}" target="_blank">{jd}</a>')
    for line in lines[section_start_line + 1:]:
        html_to_display += line
        if '<hr' in line:
            htmls_to_display.append(html_to_display)
            break

Figure 1: Antenna autocorrelation metric spectra and waterfalls.¶

These figures are reproduced from auto_metrics notebooks. For more info on the specific plots and metrics, see those notebooks (linked at the JD). The most recent 100 days (at most) are shown.

In [12]:
for i, html_to_display in enumerate(htmls_to_display):
    if i == 100:
        break
    display(HTML(html_to_display))

Antenna 59: 2459864

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 50.450052 6.842178 50.450052 -1.089873 2.077257 0.449751 3.677281 12.050349 5.471731

Antenna 59: 2459863

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 24.406391 24.406391 5.381905 -0.810319 0.508891 5.804978 3.713988 19.278061 18.066216

Antenna 59: 2459862

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Temporal Discontinuties 21.662981 2.631815 10.328683 0.676779 0.657894 4.714494 3.801800 21.662981 14.692303

Antenna 59: 2459861

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 25.855451 1.532222 25.855451 0.810004 -0.720041 1.529031 0.120019 3.220936 2.064155

Antenna 59: 2459860

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 21.904884 21.904884 2.282970 1.054804 0.774635 7.286083 0.971598 3.163818 2.950127

Antenna 59: 2459859

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 20.057258 20.057258 1.844305 -0.742431 1.024146 -0.010641 1.563357 5.703602 7.834898

Antenna 59: 2459858

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 27.201683 1.551202 27.201683 0.865188 -0.791642 2.403911 0.291481 5.056602 3.708576

Antenna 59: 2459857

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 2.431043 -0.630875 2.431043 -0.642617 -1.325867 0.733186 1.134683 1.017405 -0.612285

Antenna 59: 2459856

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 36.872522 36.872522 4.123114 1.390223 1.187818 2.715057 0.874903 4.353870 6.058571

Antenna 59: 2459855

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 36.769955 5.263141 36.769955 0.624491 1.362815 1.398526 1.049044 3.828587 2.532185

Antenna 59: 2459854

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 43.721341 4.703118 43.721341 0.095387 1.820185 0.913826 0.915799 1.714877 1.051646

Antenna 59: 2459853

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 30.645108 3.140462 30.645108 0.250182 2.434389 0.566680 2.991383 5.984501 4.194870

Antenna 59: 2459852

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 19.368184 19.368184 3.246582 2.186928 0.127206 5.347286 0.468716 5.050586 0.591528

Antenna 59: 2459851

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 21.408526 21.408526 1.992630 2.651270 0.392886 7.762365 1.898464 5.350345 3.116609

Antenna 59: 2459850

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 26.038955 26.038955 3.197912 2.226979 0.241278 3.710470 0.146991 2.885598 2.037742

Antenna 59: 2459849

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 38.165788 38.165788 3.916434 5.994384 1.500904 3.409707 1.421007 6.934457 9.013549

Antenna 59: 2459848

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 24.505006 3.267827 24.505006 2.352809 1.917040 -0.004557 3.438660 4.921548 4.790111

Antenna 59: 2459847

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 34.772840 3.457578 34.772840 1.715500 2.228304 1.567689 4.486032 4.807589 5.637892

Antenna 59: 2459846

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 27.210792 27.210792 3.531416 1.010270 1.399444 4.948944 2.089112 4.175592 3.060530

Antenna 59: 2459845

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Shape 23.526629 5.538594 23.526629 2.737583 2.335410 1.254090 3.733709 10.241235 22.658554

Antenna 59: 2459844

Ant Node A Priori Status Worst Metric Worst Modified Z-Score ee Shape Modified Z-Score nn Shape Modified Z-Score ee Power Modified Z-Score nn Power Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Discontinuties Modified Z-Score nn Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance ee Power 6.765837 4.262356 1.219994 6.765837 1.487968 1.107066 1.292099 -0.687745 0.549580

Antenna 59: 2459843

Ant Node A Priori Status Worst Metric Worst Modified Z-Score nn Shape Modified Z-Score ee Shape Modified Z-Score nn Power Modified Z-Score ee Power Modified Z-Score nn Temporal Variability Modified Z-Score ee Temporal Variability Modified Z-Score nn Temporal Discontinuties Modified Z-Score ee Temporal Discontinuties Modified Z-Score
59 N05 digital_maintenance nn Temporal Discontinuties 24.254117 19.158184 1.270081 -0.029407 10.781159 1.333786 3.770363 24.254117 4.581501

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